title: Phrasal Paraphrase Based Question Reformulation for Archived Question Retrieval.
abstract: Lexical gap in cQA search, resulted by the variability of languages, has been recognized as an important and widespread phenomenon. To address the problem, this paper presents a question reformulation scheme to enhance the question retrieval model by fully exploring the intelligence of paraphrase in phrase-level. It compensates for the existing paraphrasing research in a suitable granularity, which either falls into fine-grained lexical-level or coarse-grained sentence-level. Given a question in natural language, our scheme first detects the involved key-phrases by jointly integrating the corpus-dependent knowledge and question-aware cues. Next, it automatically extracts the paraphrases for each identified key-phrase utilizing multiple online translation engines, and then selects the most relevant reformulations from a large group of question rewrites, which is formed by full permutation and combination of the generated paraphrases. Extensive evaluations on a real world data set demonstrate that our model is able to characterize the complex questions and achieves promising performance as compared to the state-of-the-art methods.
authors.affiliation: Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, Harbin City, Heilongjiang, China.
authors.forename: Yu
authors.initials: Y
authors.lastname: Zhang
authors.forename: Wei-Nan
authors.initials: WN
authors.lastname: Zhang
authors.forename: Ke
authors.initials: K
authors.lastname: Lu
authors.forename: Rongrong
authors.initials: R
authors.lastname: Ji
authors.forename: Fanglin
authors.initials: F
authors.lastname: Wang
authors.forename: Ting
authors.initials: T
authors.lastname: Liu
mesh_terms: D057225:Data Mining / Q000379:methods*; D006801:Humans; D007802:Language; D012984:Software